The Research On Vehicle License Plate Recognition System Based On Rough Set And Neural Network

Rough Set theory, a new mathematical tool dealing with vagueness and uncertainty, was introduced by Poland scholar Z. Pawlak. It can describe the significance of different attributes and reduce the dimension of knowledge space. But its pattern recognition based on rule matching is sensitive to disturb. Neural network has better robustness and widely used in pattern recognition. Combining these two theories not only simplifies the complexity of information processing but also improves the precision of information processing. Vehicles' License Plate Recognition (LPR) system is an important application of computer vision and pattern recognition in the intelligent transportation management. So, the research on LPR system based on rough set and neural network is of certain academic and practice value.In this article, rough set and neural network are combined and used in the LPR system. Some work has finished as following.Firstly, four binarization algorithms are presented, including minimum cross-entropy, maximum between-class cross-entropy, minimum fuzzy divergence and maximum between-class fuzzy divergence. Secondly, an effective locating algorithm is presented based on color analysis, which has fewer limits to the car size, the car position in the image and the image background. Thirdly, two attribute discretization algorithms are presented, which are based on NCL clustering method and self-organizing feature map (SOFM) neural network respectively. And three modified attribute reduction algorithms are presented, including modified algebraic algorithm, weighed sum of attribute significance algorithm and modified discernible matrix algorithm. Fourthly, two effective character recognition methods are presented. In the first method, rough set is used to solve the knowledge acquisition and reduction, and neural network is used to solve the pattern recognition. In the second method, a rough fuzzy neural network classifier is introduced, which combines the rough set, neural network and fuzzy logic. It can efficiently improve the robustness and recognition rate of the LPR system.